efficiency of production factors use of corn farming in

Jurnal Lahan Suboptimal: Journal of Suboptimal Lands

ISSN: 2252-6188 (Print), ISSN: 2302-3015 (Online, www.jlsuboptimal.unsri.ac.id)

Vol. 9, No.1: 89-101 April 2020

DOI: https://doi.org/10.33230/JLSO.9.1.2020.452

Efficiency of Production Factors use of Corn Farming in Type C Tidal

Land, Banyuasin Regency

Efisiensi Penggunaan Faktor Produksi Usaha Tani Jagung di Lahan Pasang Surut Tipe C

Kabupaten Banyuasin

Yudhi Zuriah Wirya Purba1*)

, Agoes Thony Ak1, Faizal Daud

1

1Faculty of Agriculture, Sjakhyakirti University, Palembang, South Sumatera 30134

*) Corresponding author: [email protected]

(Received: 10 October 2019, Accepted 25 March 2020)

Citation: Purba YZW, Thony Ak A, Daud F. 2020. Efficiency of production factors use of corn farming in

type C tidal land, Banyuasin Regency. Journal of Suboptimal Lands 9(1): 89-101.

ABSTRAK

Tujuan penelitian ini adalah menganalisis faktor-faktor produksi yang mempengaruhi

produksi usaha tani jagung, efisiensi dan elastisitas penggunaan faktor produksi usaha tani

jagung. Penelitian ini dilakukan di Desa Mulia Sari, Kecamatan Tanjung Lago, Kabupaten

Banyuasin, sampel random 30 petani jagung dari 320 anggota populasi. Hasil penelitian

menunjukkan faktor-faktor yang memiliki pengaruh positif yang signifikan terhadap

produksi jagung adalah penggunaan pupuk urea dan pupuk SP-36, sedangkan penggunaan

herbisida memiliki efek negatif,penggunaan tenaga kerja, pupuk KCl, insektisida dan ZPT

tidak berpengaruh signifikan, penggunaan tenaga kerja dan ZPT secara teknis tidak

efisien,penggunaan pupuk urea, SP-36, KCl, insektisida dan ZPT secara teknis efisien.

Secara keseluruhan penggunaan faktor produksi pada usaha tani jagung secara teknis,

ekonomis dan harga efisien, dan nilai elastisitas sebesar 0,925.

Kata kunci: efesiensi, elastisitas, pasang surut, pengaruh

ABSTRACT

The purpose of this study were to analyze the production factors affecting corn farming,

the efficiency and elasticity of the use of production factors in corn farming. This research

was conducted in Mulia Sari Village, Tanjung Lago District, Banyuasin Regency. A

random sample of 30 corn farmers from 320 populations was employed in this research.

The results showed factors that had a significant positive effect on corn production were

the urea fertilizer and SP-36 fertilizer, while the factor of herbicides had a negative effect,

and factors of labor, KCl fertilizer, insecticide and ZPT had no significant effect, labor and

growth regulator were technically inefficient, while the urea, SP-36, KCl fertilizers, and

insecticides were technically efficient. Overall, the use of production factors in corn

farming was technically efficient in term of economy and price with the elasticity value by

0.925.

Keywords: efficient, elasticity, influence, tidal land

INTRODUCTION

Corn (Zea mays L.) is one of the most

important food crops in the world, besides

wheat and rice. As the main carbohydrate

source in Central and South America, corn

is also an alternative food source of staple

food of the residents of several regions in

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90 Purba et al.: Efficiency of Production Factors use of Corn Farming

Indonesia (for example in Madura and Nusa

Tenggara) (Khalik, 2010). Apart from being

a source of carbohydrates, corn is also

cultivated for animal feed (forage or cob),

oil (taken from seeds), flour (made from

seeds, known as cornstarch), and industrial

raw materials (from seed flour and cob

flour) for pentose, which is used as a raw

material for furfural production. Corn

which has been genetically engineered is

also now cultivated for pharmaceutical

ingredients (Soekartawi, 2003; Adisarwanto

and Yustina, 2004).

South Sumatra, as one of the provinces

with diverse agroecosystems, is one of the

contributors of national corn production.

Based on statistical data, the area of corn

harvest in South Sumatra in 2017 was

138,232 hectares. (Central Bureau of

Statistics, 2018). Some mainstay areas of

corn development are South OKU Regency

with harvest area of 39,414 hectares, East

OKU Regency of 25,667 hectares, and

Banyuasin Regency of 20,510 hectares.

Other regencies in South Sumatra Province

are less suitable for the development of

maize plants, only limited to side crops and

focus more on the development of other

food crops.

As one of the regencies that has become

the mainstay of the South Sumatra region in

terms of corn production, the Banyuasin

Regency government attempted to

increased corn production in order to

achieve the goal of self-sufficiency in corn.

In 2017, the Minister of Agriculture of the

Republic of Indonesia announced the

integration of corn plants between Palm Oil

and Rubber plants, so that the area of corn

plants in Banyuasin Regency during 2017

reached 20,000 hectares.

Observing such an agroecosystem,

Banyuasin Regency still has the potential

for the development of corn after other food

crops. In fact, with an increase in cropping

index (IP), it is possible to plant corn after

rice or vice versa (Akil and Dahlan, 2009).

In fact, in 2017 the implementation of the

program reached and even beyond the

target of planting area of 22,712 hectares

(Statistical Report of the Banyuasin Distan,

2018).

Banyuasin Regency corn production in

2017 reached 147,605.7 tons, which means

that the average production per hectare was

6.75 tons per hectare, making it higher than

the national average of 6.57 tons per

hectare. This showed that the corn

commodity (Goldsworthy, 2000) is very

suitable to be developed in Banyuasin

Regency, especially in sub-districts that

have not yet reached the planting index (IP)

200 such as Air Saleh District, Muara

Padang District, Makarti Jaya District, Air

Telang District, Sumber District Marga

Telang and part of Banyuasin II District.

In order to develop production through

the application of the 200 Planting Index, in

the context of increasing the economic

performance of maize commodities, it is

necessary to conduct research on the

relationship of various micro factors, both

aspects of production such as productive

area, new area, replanting corn production,

and aspects of corn production related with

the demand and price of corn as well as

aspects of the corn trade. The research was

conducted in tidal land type C of Mulia Sari

Village, Tanjung Lago District, Banyuasin

Regency.

Tidal land type C is the tidal land with

the condition of the land not flooded but the

depth of ground water at high tide is less

than 50 cm (Munir, 2001; Hardjowigeno,

2003; Ermanita et al., 2004). Efforts to

increase production can be done by means

of intensification by increasing the use of

production factors such as labor, capital and

technology on a fixed area of land, and

extensification by extending the planting

area without adding capital, labor and

technology (Rukmana, 2009). This

phenomenon created the chance of research

on how to manage corn commodities so that

farmers can use production factors

efficiently. Based on this background, this

study aims to analyze the factors of

production that affect the production of

corn farming in Mulia Sari Village, and

analyze the efficiency and elasticity of the

Jurnal Lahan Suboptimal: Journal of Suboptimal Lands 9(1) April 2020 91

use of corn farming production factors in

Mulia Sari Village.

MATERIALS AND METHODS

This research was conducted in the tidal

land of Mulya Sari Village, Tanjung Lago

District, Banyuasin Regency, with

consideration that it is one of the biggest

corn producing districts in Banyuasin

Regency. The research method is a survey

(a method by taking a sample of a fairly

large population) (Babbie, 1990; Sugiyono,

2013). The sampling method uses a random

sampling, with a total sample of 30 sample

farmers from 320 population members

(Nasution, 2012; Cochran, 1965). Data in

the field was obtained through direct

interviews with sample farmers using a list

of questions or questionnaires that had been

prepared (Soekartawi, 2002). Analysis of

the data illustrated the relationship between

input and output in the production process

known as the Cobb Douglas production

function application, through the SPSS 16.0

program for Windows, with the following

equation (Soekartawi, 2003):

Y = .

This equation was then changed in the form

of logarithm with formula as follows:

Ln Y = Ln a + b1LnX1 +b2LnX2 + b3LnX3+

b4LnX4+ u

To estimate the factors that influence

output (Y), the Cobb-Douglas model is

appropriate, because this model is the most

relevant model. Furthermore, the MLE

(Maximum Likelihood) method will present

the coefficients of each of these factors that

affect production or the dependent variable.

Uji Efisiensi

Efficiency is a relative concept. Testing

of efficiency is performed to see how the

combination of certain factors of production

can produce optimal output. There are three

concepts of efficiency, namely technical

efficiency (ET), economic efficiency (EE),

and price efficiency (EH). Technical

efficiency is a production process using a

combination of only a few (smallest) input

sets to produce the largest output (in this

study the value of technical efficiency is

automatically seen from the output of the

regression coefficient). Price efficiency is a

production process using a certain level of

output that can produce similar output, with

lower costs. In this study the value of price

efficiency is calculated by the formula:

NPM = 𝒃𝒀𝒑𝒀𝒙Px

b : coefficient of elasticity

Y : output

Py : output price

X : production factor

Px : price of production factor

Economic efficiency will be achieved if

technical efficiency and price efficiency

have been achieved, calculated by the

equation:

EE = ET x EH

If the efficiency has value of more than

1, it means the use of inputs needs to be

increased, if the value of efficiency = 1, it

means the optimal input allocation, if the

efficiency value less than 1, it means the

use of inputs needs to be reduced

(Soekartawi, 2003).

In accordance with the initial hypothesis

in this study that if the average efficiency

(technique, price, and economy) values are

not equal to one, then the hypothesis is

accepted. But if the value of efficiency

(technique, price, and economy) is on

average equal to one, then the hypothesis is

rejected.

Approching Model The approaching model in this research

was the diagrammatic approaching model

as shown in Figure 1.


Figure 1. Diagrammatic model of the relationship between corn farming and its technical, allocative, and

economical efficiency.

RESULTS AND DISCUSSION

Factors That Influence Corn Production

There are seven factors that werw

suspected to influence the productivity of

corn farming, namely the amount of labor

(TK), the dose of Urea fertilizer (DU), the

dosage of SP-36 (DS), the dose of KCl

fertilizer (DK), the dose of herbicide (DH),

the dose insecticide (DI), and dose of

growth regulator (DZ).

Variable land area was not included

because the area of land owned by the same

farmers was one hectare. Likewise with the

number of seeds, the amount per hectare

used was the same. The magnitude of the

influence of these factors was determined

by the analysis of the multiple regression

equation and because the equation that

would be assumed to be a production

function, multiple Cobb-Douglas type

regression was employed. The results of the

alleged regression equation using the help

of a SPSS computer program (Table 1).

The alleged results in Table 1 can also

be presented in the form of the guessed

regression equation as follows:

Y -2,082TK-,065

DU,826

DS,247

DK,134

DH-

,404DI

,051DZ

,136 t-1,877-0,065

0,8260,2470,134-0,4040,0510,136

R2 = 0,475; Fhit = 2,840; df = 29; dw =

1,823

or in the linier form as follows:

Y = -2,082 - 0,065LogTK + 0,826LogDU +

0,247LogDS + 0,134LogDK -

0,404log DH+ 0,051Log DI +

0,136Log DZ

Farming

Type C Land Production Factors

Price

Revenue

Efficiency

Technical Allocative Economical


Table 1. The estimation result of factors affecting the production of corn farming in Mulia Sari Village,

Tanjung Lago District, Banyuasin Regency, 2018

Remarks: A = significant at α = 10%, B = significant at α = 15%, C = significant at α = 25%

Table 2. The average use of production factors, input price, product price, and product quantity in Mulia Sari

Village, Tanjung Lago District, Banyuasin, Regency, 2018

Production Factor Usage quantity

Dosage (kg or ltr/ha) Price (IDR/kg/ltr)

Labor 25,37 92.497

Urea Fertilizer 410,00 2.000

SP-36 Fertilizer 316,67 2.400

KCl Fertilizer 85,00 9.000

Herbicide 4,67 60.000

Insecticide 0,92 234.667

Growth Regulator 3,90 58.928

Remarks : Pproduction = 4,68 ton, Price= Rp. 3.272.155/ton

Table 3. The value of efficiency of the use of production factors in Mulia Sari Village, Tanjung Lago District,

Banyuasin Regency, 2018

Xi PR

(Y/Xi)

PM

(βxPR)

NPM

(PMxHy) Hx

Eficiency

Index t statistic Criteria

25,37 0,184 - 0,012 - 39.212 92.497 - 0,42 - 1,44 E

410,00 0,011 0,009 30.830 2.000 15,41 1,84 E

316,67 0,015 0,004 11.936 2.400 4,97 1,32 E

85,00 0,055 0,007 24.124 9.000 2,68 0,79 E

4,67 1,002 -0,405 -1.324.783 60.000 - 22,08 -1,31 E

0,92 5,102 0,260 851.391 234.667 3,63 0,22 E

3,90 1,199 0,163 533.635 58.928 9,06 0,96 E

Remarks: 1. Labor, 2.Uurea, 3. SP-36, 4. KCl, 5. Herbicide 6. Insecticide, 7. growth regulator, t-tabel

(5%)=2,04, E= Efficiency

Level of Efficiency and Elasticity in the

use of Production Factors in corn

farming

The large number of inputs used in a

production process will affect the amount

of output produced. Efficiency is an attempt

to use the smallest possible input to obtain

the maximum output. To determine the

technical efficiency of the use of production

factors can be done by looking at the

elasticity of production that can be known

from the regression coefficients in the

Cobb-Douglas type regression equation.

Price or allocative efficiency can be

achieved when the Marginal Product Value

(NPM) is the same as the Input Price (Hx).

Conclusions about the condition of

inefficient use of production inputs in order

to apply to the population, then the index k

was done using the t test.

If the index value t is smaller or equal to

the value of t table at a certain level of

confidence, then the use of production

inputs is allocatively efficient, conversely if

the t-index value is greater than t table, then

it is inefficient/inefficient. More clearly the

Variable Regression

Coefficient t value

Sig. Remarks

Intercept -2,082 -1,877 ,074 A

Log Labor(LTK) -,065 -,212 ,834 -

Log Urea fertilizer dosage (LDU) ,826 1,744 ,095 A

Log SP-36 fertilizer dosage (LDS) ,247 1,241 ,228 C

Log KCl fertilizer dosage (LDK) ,134 ,855 ,402 -

Log Herbicide dosage (LDH) -,404 -1,182 ,250 C

Log Insecticide dosage (LDI) ,051 ,277 ,785 -

Log Growth Regulator dosage (LDZ) ,136 ,962 ,347 -

R2 = 0,475; F=2,840; df = 29; dw = 1,823

F-tabel (5%) = 2,4638; t-tabel(5%)= 2,04


results of price efficiency analysis can be

seen in the following table.

The use of Input

The input of corn farming production in

this research location were labor, Urea

fertilizer, SP-36, KCl, herbicide, insecticide

and growth regulator (ZPT). The land was

not included in the analysis because the

area is the same among farmers, and so is

the seed of the same number of uses per

hectare between farmers. Average dose of

input use and price are shown in Table 2.

Table 3 shows the value of the efficiency of

the use of corn farming production factors

(Table 3).

Discussion

Regression Analysis

Based on the summary of the regression

results presented or the alleged regression

equation in Table 1, economically this

estimation equation was satisfactory as seen

from the magnitude of all estimated

parameter values close to the value of one,

which refers to elastic criteria ie if it is

smaller than one it is called inelastic,

whereas if it is greater than one, it is called

elastic. Values greater than one will not be

too far from one let alone up to two digits,

the expected parameter sign, the sign can be

positive or negative. If it is negative,

technically the use of the production factor

is excessive, whereas if it is positive it can

mean that the use has not been efficient or

technically efficient.

This means that economically, the

results of the alleged equation had no

problem. Statistically, the results of this

alleged equation had been relatively good,

although the coefficient of determination

(R2) was relatively small at 47.50 percent,

this indicated the use of labor and the dose

of use of inputs (Urea fertilizer, SP-36,

KCl, herbicide, insecticide and growth

regulators) can explain 47.50 percent of the

variation in corn productivity, the

remaining 52.50 percent is caused by other

factors.

Factors did not included in the equality

model such as land area, number of seeds,

farmers 'experience, farmers' education and

guidance provided by the instructor. The

relatively small value of the coefficient of

determination does not matter if the

purpose of the study was not to make a

forecast as in the purpose of this study. So

that the R2 value generated by the alleged

equation below 50 percent was not used as

an indicator of dominant statistical criteria.

Then from the joint test represented by the

F test, it was good because it was

statistically significant at the 95 percent

confidence level (α = 5 percent).

Furthermore statistical criteria based on

individual tests namely the t test were also

quite satisfactory because by using the

lowest level of confidence 75 percent (α =

25 percent), three independent variables

were significant and four were not

statistically significant.

Although the number of significant

independent variables was less than the

insignificant, but from other criteria,

especially econometric criteria, namely

multicolliniarity does not occur, then this

condition was not a problem (it can be

concluded that the statistical equation is

satisfactory). Based on econometric criteria,

the results of the alleged equation were also

satisfactory. Econometrics criteria can be

seen from the presence or absence of

multicollinearity, autocorrelation and

heteroscedasticity problems.

Multicollinearity problems (Barbara et

al., 1983) can be seen from the Tolerance

and VIF (Variant Index Factor) results of

data processing with SPSS. Tolerance value

cannot be less than 0.1, while VIF value

cannot be more than 10. If this is violated,

there will be a multicollinearity problem.

Regression results show that tolerance

values range from 0.29 to 0.48, while VIF

values range from 1.9 to 3.5 (this means

that the assumed equation does not have

multicollinearity problems). The second

econometrics criterion, the autocorrelation


problem, can be seen from the Durbin

Watson (dw) value of the alleged regression

equation. The alleged equation does not

indicate an autocorrelation problem if the

dw value is close to 2, conversely if it

approaches 0 a positive autocorrelation will

occur and if it approaches 4 there is a

negative autocorrelation. The Durbin

Watson test value obtained from the

regression results is 1.82 (the dw value is

close to 2), so it can be concluded that there

is no autocorrelation problem in the results

of the alleged regression obtained.

Furthermore, the last criterion of

econometrics is the problem of

heteroscedastasis.

The problem of heteroscedasticity can be

seen from the results of data processing

with SPSS and if the image of the

relationship between standardized residual

values (regression studentized residuals)

with standardized predicted values does not

have a certain pattern. Based on the

resulting image, there was no pattern, so it

could be concluded that there was no

heteroscedastity problem in the obtained

equation. It could be concluded that the

econometric equation of the regression

equation was satisfactory. Based on the

description that discusses the three criteria,

namely economic criteria, statistics and

econometrics, it can be concluded that the

regression equation obtained was

satisfactory, so it can be interpreted the

value of the influence of each. The

following will discuss the effect of each

variable on the production of corn.

The Influence of Labor Usage

The influence of labor usage on corn

productivity was -0.065 which was the

estimated parameter value. This value after

being tested with the t test turned out to be

insignificant at the level of α = 25 percent

because of the significant level of 0.834,

this meant that the productivity of corn was

not affected by the amount of outpouring of

labor. The average workforce used at the

study site was 25.37 Days of Workers

(HOK), while according to the standard it

was 62 HOK, meaning the amount of

workforce use was less than half lower than

the recommendation, so it was natural for

workers to have no significant effect on

corn productivity. Labor was used for land

preparation, planting, maintenance and

harvesting. Because of maintenance

activities, the use of herbicides and growth

regulators (ZPT) were not in accordance

with the recommendations so that the use of

labor was also reduced.

The Influence of the Dose of Urea

Fertilizer

The effect of the dose of the use of urea

fertilizer on corn productivity can be seen

from the estimated parameter values of the

variable which was 0.826. This value

turned out to be statistically significant at α

= 0.10 (90 percent confidence level).

Because the value of this parameter was

automatically an elasticity value, corn

production was not responsive to changes

in the dose of Urea fertilizer, meaning that

the parameter value if the dose of urea

fertilizer increased by one percent, then

corn production would increase by 0.826

percent or vice versa. Based on the data, the

dose of using urea fertilizer was an average

of 410 kg per hectare. When compared to

the recommended dosage (300-450) kg per

hectare, the dose of the use of urea fertilizer

by farmers was in accordance with the

recommended dosage (Sutejo, 2002).

The Influence of the Dose of SP-36

Fertilizer

The influence of the use of SP-36

fertilizer dosage can be seen from the

estimated parameter values of the variable

in the regression equation that was equal to

0.247. This value was then performed a t

test and it turned out that the use of

fertilizer was significant at α = 25 percent.

This means that if the SP-36 fertilizer dose

was added by one percent, then corn

production would increase by 0.247 percent

or vice versa, cateris paribus. The average

use of SP-36 fertilizer was 316.67 kg per

hectare. The recommended dosage for SP-


36 fertilizer was 100 kg per hectare, this

meant the dosage applied by farmers was

three times more than the recommendation.

The Influence of the Dose of KCl

Fertilizer

The effect of the dose of the use of KCl

fertilizer can be seen from the estimated

parameter values of the variable in the

regression equation that was equal to 0.138.

This value was then performed a t-test and

it turned out that the use of KCL fertilizer

was not significant to a maximum level of

confidence of 75 percent. This meant that

adding or reducing the KCl fertilizer dosage

will not significantly affect the corn

production. In this condition, not all

fertilizer applied to the soil can be absorbed

by plants. According to Olson and Sander

(1988), several factors that influence the

availability of nutrients in the soil to be

absorbed by plants include the total supply

of nutrients, soil moisture and aeration, soil

temperature, and physical and chemical

properties of the soil (all of these factors are

generally applicable to each element

nutrient).

The Influence of the Dose of Herbicide

The effect of the dose of herbicide use

on corn productivity can be seen from the

estimated parameter values of the variable

which was -0.404. This value turned out to

be statistically significant at α = 0.25 (75

percent confidence level). Because the

automatic parameter value was the

elasticity value, maize production was not

responsive to changes in herbicide dosage.

The meaning of the parameter value if the

dose of herbicide increases by one percent,

then corn production will decrease by 0.404

percent or vice versa (in this condition the

parameter value has a negative effect on

plants) (Sastroutomo, 1990).

The Influence of the Dose of Insecticide

The effect of the dose of insecticide use

can be seen from the estimated parameter

values of the variable in the regression

equation that was equal to 0.051. This value

was then carried out a t test and it turned

out the use of insecticides was not

significant to a maximum level of

confidence of 75 percent. This meant the

addition or reduction of insecticide doses

did not significantly affect the corn

production (Sastroutomo, 1990).

The Influence of the Dose of Plant

Growth Regulators (PGR) The effect of the use of growth regulator

on corn productivity was 0.136. This value

after being tested with the t test was

apparently not significant at the α = 25

percent level, because the significance level

was 0.347. This means that corn

productivity was not affected by the PGR

dose, so with an estimated parameter value

of less than one which was 0.069, it meant

that production was not responsive to

changes in the PGR dose. The average dose

of PGR used at the study site was 3.90 liters

per hectare (PGR is an element of the

hormone also known as phytohormone

which has a significant effect on

stimulating, inhibiting or changing plant

growth, development and movement). PGR

is different from plant nutrients or nutrients,

both in terms of function, shape, and its

constituent compounds. Each type of plant

has a different response to the growth

regulators given. (Dwijoseputro, 1990).

The Analysis of Technical Efficiency The elasticity of the Cobb-Douglas type

production function Was shown from the

regression coefficient values of each

production factor. The factors of corn

production were as follows.

a. Labor

The estimated parameter values in the

Cobb Douglas type regression equation

were elastic values, so the elasticity value

of the labor variable as presented in Table 1

was -0.065. The value of the elasticity of

labor production was negative (Ep <0)

indicating that the use of 25.37 HOK

laborers was irrational or is in region III,

the area of increasing yields which was


increasingly reduced. So that the workforce

on this type of land must be reduced.

Though the use of labor was still below the

standard of use of labor for corn plants

which amounted to 62 HOK. This was

because in tidal land more labor was needed

for processing and maintaining land

(Ridwan, 1992; Saidah et al., 2004;

Rukmana, 2009).

b. Urea Fertilizer

The estimated parameter value of the

Urea fertilizer variable as presented in

Table 1 was 0.826. The elasticity value of

urea fertilizer production was between zero

to one (0 <Ep <1) or is in the production

area II, namely the rational area. This

means the dosage of using Urea fertilizer

was 410 kg per hectare. The dosage of the

use of this fertilizer was in accordance with

the recommended dosage of Urea fertilizer

for corn plants which was (300-450) kg per

hectare.

c. SP-36

The estimated parameter values for the

SP-36 fertilizer variable as presented in

Table 1 was 0.247. The elasticity value of

this fertilizer production was between zero

to one (0 <Ep <1) or was in the area of

production II, namely the rational area. This

means that the use of SP-36 fertilizer was

316.67 kg per hectare. This dosage of

fertilizer use was more than three times the

recommended dosage for corn plants which

is only 100 kg per hectare (this was because

in the tidal land, more phosphorus was

needed compared to other mineral fields).

d. KCl


KCl fertilizer variable as presented in Table

1 is 0.134. The elasticity value of this

fertilizer production, which was between

zero to one (0 <Ep <1) or is in the area of

production II, namely the rational area. This

means that the use of KCl fertilizer which

was 85 kg per hectare was technically

efficient. The dosage of the use of fertilizer

was almost close to the recommended

dosage for corn, which was 100 kg per

hectare.

e. Herbicide The estimated parameter value of the

herbicide variable as presented in Table 1

was -0,404. The elasticity value of this

herbicide production was negative, meaning

that it was in the production area III (Ep <0)

or irrational region. This means that the

dosage of herbicide used had exceeded

recommended or technically inefficient.

The dosage of herbicide used was 4.67

liters per hectare, exceeding the

recommended dosage of only 2 liters per

hectare.

f. Insecticide


insecticide variable as presented in Table 1

was 0.051. The elasticity value of this

insecticide production was positive and had

a value between zero and one (0 <Ep <1),

which means that it was in production area

II or rational area. This means the dosage of

herbicide use was technically efficient. The

insecticide dose used was 0.92 liters per

hectare, almost close to the recommended

dose of 1 liter per hectare.

g. Plant Growth Regulators (PGR)


PGR variable as presented in Table 1 was

0.136. The PGR production elasticity value

was positive and had a value between zero

and one (0 <Ep <1), which means that it

was in the production area II or the rational

area. This means that the dose of using

PGR was technically efficient. The PGR

dose used wad 3.90 liters per hectare in

type C land, in accordance with the

recommended dosage (3-4 liters) per

hectare.

Price Efficiency

Furthermore, based on Table 3, all

production factors used in corn farming

have reached price efficiency, which are as

follows:


a. Labor Usage

Based on the calculation of the

efficiency of the use of corn production

factors as shown in Table 3, the efficiency

index value for the labor variable was -0.42.

The efficiency index value shows the ratio

between the value of marginal products

(NPMx) and factor prices (Hx). The

efficiency index value was smaller than 1.

Then to find out the statistical value

expressed by the t-count value, the

different efficiency index value (k) was

tested.

Furthermore, the t-count value is

compared with the t-table value. The level

of confidence used in this test was 95

percent or at the real level of 5 percent (α =

5%). The tcount obtained was -1.44 at the

95 percent confidence level. Furthermore,

this value was compared to the value of

table, where the value was 2.04, this means

the value of count was smaller than table.

This shows that the efficiency index value

was not significantly different from one (k

= 1) which means that the use of an average

labor of 25.37 HOK per hectare was

allocatively efficient or has reached price

efficiency.

Although according to the recommended

number of workers was 62 HOK per

hectare. HOK depends on the number of

workers, working days, and hours of work

per day, but at the location of this study it

seemed that the use of less than half the

recommendations was efficient (Ridwan,

1992; Saidah et al., 2004; Rukmana, 2009).

b. Urea Usage

The use of urea fertilizer has been

allocatively efficient or reached price

efficiency. This was because the calculated

value of the efficiency index test (1.84) was

still smaller than the value of 2.04 at a 95

percent confidence level. The fertilizer dose

used by farmers is 410 kg per hectare

according to the recommendations, which

was between (300-450) kg per hectare.

According to Azzaimy (2016), nitrogen or

Urea fertilizer was usually used in three

stages of administration, the first stage was

75 kg, the second stage was 150 kg, and the

third stage was 75 kg, so that in one hectare

the dose of urea fertilizer was 300 kg. This

means that the Urea fertilization dose

carried out will result in additional costs

incurred was still smaller than the income

received (Sutejo, 2002) as a result of the

addition of a unit of additional costs of

these inputs.

c. SP-36 Usage

The use of SP-36 fertilizers had also

been allocatively efficient or achieved price




percent confidence level. The fertilizer

dosage used by farmers, which was 316 kg

per hectare, had exceeded the

recommendation of only 100 kg per hectare

(Azzamy, 2016). In this condition, although

the dosage exceeds the recommendation,

the dosage would result in additional costs

incurred was still smaller than the income

received as a result of adding one unit cost

of the SP-36 fertilizer.

d. KCl Usage

The efficiency index value for KCl

fertilizer or the comparison of the marginal

product value (NPMx) with the price of

production factor (Hx) was 2.68, which

means the efficiency index value was

greater than 1. Then a different efficiency

index value was tested using the t test and

the real level used was equal to 5%, then

the tcount value obtained was 0.79. If the

tcount value was compared to the ttable

value (2.04), then the efficiency index was

not significantly different from one (k = 1)

because the tcount was smaller than the

ttable. This means that the use of KCl

fertilizer at the research location was

efficient by using allocative efficiency

criteria. The dose of KCl fertilizer used by

farmers was 85 kg per hectare, while the

recommended dosage was (50-100) kg per

hectare (Azzamy, 2016). This means that

the dose used by farmers was in accordance

with what was recommended.


e. Herbicide usage

The use of herbicides had been

allocatively efficient or reached price

efficiency. This is because the calculated

value of the efficiency index test (-1.31)

was still smaller than the value of 2.04 at a

95 percent confidence level. The dose of

herbicide used by farmers was 4.67

hectares, which was twice the

recommended dosage of only 2 liters per

hectare (Dessy, 2015).

This means that at the dose of the use of

the herbicide, even though more than the

recommendation will still result in

additional costs incurred less than the

income received as a result of the addition

of one additional unit of input costs.

f. Insecticide usage

The use of insecticides had also been

allocatively efficient or achieved price




percent confidence level. The insecticide

dose used by farmers was 0.92 liters per

hectare which was already close to the

recommended dosage of 1 liter per hectare

(Dessy, 2015).

This means that at the dose of the use of

the insecticide even though a little smaller

than the recommendation would result in

additional costs incurred less than the

income received as a result of the addition

of a unit of additional costs of these inputs.

g. PGR usage The use of PGR had also been

allocatively efficient or achieved price




percent confidence level. The dose of ZPT

used by farmers was 3.90 liters per hectare

which was in accordance with the

recommendations, namely (3-4) liters per

hectare (Sitompul and Guritno, 1995). This

meant that at the dose of the use of the ZPT

it would still produce additional costs

incurred less than the income received as a

result of the addition of one additional unit

of input costs.

The value of production elasticity

Based on the elasticity value of each of

the factors of production, the total elasticity

of production can be obtained. The total

elasticity of production is obtained by

adding up the elasticity of all the factors of

production. The following formula is the

total elasticity of corn production in tidal

land, namely:

E = β1 + β2 + β3 + β4 + β5 + β6 + β7

Keterangan :

E = Total production elasticity

β1 = labor elasticity

β2 = urea fertilizer elasticity

β3 = SP-36 fertilizer elasticity

β4 = KCl fertilizer elasticity

β5 = herbicide elasticity

β6 = insecticide elasticity

β7 = PGR elasticity

Then the value of production elasticity is:

E = -0,065 + 0,826 + 0,247 + 0,134 - 0,404

+ 0,051 + 0,136

E = 0,925

Total elasticity of production was 0.925

which means the value is between 0 and 1

or the region of production I or return to

scale. Thus technically the use of

production facilities by corn farmers was

efficient.

CONCLUSION

Factors that had a significant positive

effect on the productivity of corn farmers in

Mulia Saria Village were the use of Urea

fertilizer and SP-36 fertilizer, while the use

of herbicides had a significant negative

effect. The use of labor, KCl fertilizer,

insecticide and PGR had no significant

effect. The use of labor and herbicides was

not technically efficient. The use of Urea,

SP-36, KCl, insecticide and PGR were

technically efficient. All factors of

production, namely labor, Urea fertilizer,

SP-36, KCl, herbicides, insecticides and


PGR were already efficient in terms of

price.

RECOMMENDATION

Based on the results of research

conducted, it is recommended:

1. In order to achieve efficient use of

production factors, farmers should

follow the recommendations and

suggestions of relevant agencies such as

the Agency for Agricultural Research,

Agriculture Services and other

institutions.

2. We recommend the use of herbicides

according to the recommended dosage in

order to obtain the maximum production

and technical efficiency of the use of

labor, the PGR needs to be reduced

because it had no real effect on

increasing production.

3. Assistance and further study was needed

in the typology of the land, especially in

the business of corn farming from

various parties such as Universities,

Agricultural Research and Development

Agency, Agricultural Services and other

Research Institutions.

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